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Renmin University of China
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Current SID-based generative recommendation models can sometimes reach future items but falter with completely unseen tokens, revealing critical gaps in cold-start handling.
Memory efficiency in 3DGS-SLAM can be dramatically improved by over 60% without sacrificing accuracy, making it viable for large-scale autonomous driving applications.
Solve SMoE load balancing at inference time without retraining by replicating heavily used experts and quantizing underutilized ones, achieving up to 1.4x imbalance reduction.
